CT Pneumonia Detection
Accurate detection and differentiation of COVID-19 from community-acquired pneumonia and other lung diseases.
Aims to accurately detect COVID-19 and differentiate it from Community-Acquired Pneumonia (CAP) and other lung diseases.
Powered by cutting-edge artificial intelligence and medical image analysis technologies, DEEPSCAN helps radiologists diagnose and assess pneumonia progress efficiently.
Worklist – Triage and Notification
- Suspected COVID-19
- Suspected Pneumonia
- No inflammation identified
QA in Lung Lobes
- Schematic diagram of infected regions
- Quantitative assessment of lesion volume and lesion proportion
- Hounsfield unit (HU) distribution within lesion regions
- Lesion volume and proportion grouped by different HU ranges
- Summary of findings in each lobe for direct use: lesion volume, proportion, and characteristics (GGO, consolidation)
Clinical DatasetsRetrospective multi-center clinical validation
Workflow & Methods
COVID-19 detection neural network – COVNet: based on deep neural network, AI-powered
- Input: CT DICOM (Non-contrast CT, Slice thickness<=3mm)
- Output: Disease Classification Labels (COVID-19, CAP, Non-Pneumonia)
Sensitivity 90%; Specificity 96%; AUC 0.96
Sensitivity 87%; Specificity 92%; AUC 0.95